The Multiverse MCP Server is a middleware server that supports running multiple MCP server instances of the same type simultaneously. Each instance has an independent namespace and configuration, achieving a completely isolated operating environment.

A data comparison tool that determines whether two sets of data come from the same entity through text normalization and semantic analysis.

This project provides a set of toolkits for crawling website content and generating Markdown documents. At the same time, it realizes the semantic search function of documents through the MCP server and supports integration with tools such as Cursor.

The MCP-Express-Adapter is a lightweight middleware for creating MCP (Model Context Protocol) servers on Express.js servers. It supports SSE communication, tool implementation, header-based authorization verification, and allows hosting multiple MCP client endpoints on the same Express server.

A server implementing the MCP protocol, supporting multiple AI agents to share and read responses to the same prompt.

A data comparison tool that evaluates whether two sets of data come from the same entity through text normalization and a language model, supporting JSON format comparison and semantic analysis.

An NPX wrapper tool for the MarkItDown MCP service without Docker, providing the same functions as the official Docker version and simplifying the installation process

A data comparison tool for identifying whether two sets of data come from the same subject, supporting text standardization, value comparison, and semantic analysis.

This is an Axiom MCP server implemented in JavaScript, used to query data through the Axiom Processing Language (APL). It provides the same functionality as the official Go version but is packaged as an npm module for easy integration into a Node.js environment.

This is a Python version of the Todoist MCP server, ported from the TypeScript version, providing the same functions through the Python MCP SDK and the Todoist Python API.

A tool for identifying whether two sets of data come from the same entity, supporting text normalization, value comparison, and semantic analysis, and integrating a language model for final judgment.